Does the author’s collaboration mode lead to papers’ different citation impacts? An empirical analysis based on propensity score matching

This study explores the impact of different collaboration modes on the cited frequency of publications. Though several studies have obtained some research results, most of them exploit association or regression-based methods, which may not lead to causal conclusions. To overcome the above challenges, we use the Propensity Score Matching (PSM) method to analyze and compare the citation frequencies resulting from four groups of collaboration models: international versus domestic, international multilateral versus international bilateral, domestic inter-organizational versus domestic intra-organizational, and domestic multi-author versus domestic single-author. More specifically, we conduct this analysis by exploring the publications with three computer science subfields from the Web of Science (WoS) database. The experimental results show that international collaboration, especially international multilateral collaboration, has a significant role in increasing the frequency of citations to scientific publications, showing that internationalization and collaboration are critical factors in the growth of the impact of the papers. Among national co-publications, collaborative publications within national organizations receive a higher citation impact. Multi-author collaborations significantly increase citation frequency compared to single-author publications. Our heterogeneity analysis across the different subfields of the computer science domain finds that the treatment effects for the three subfields differ modestly and mostly significant from the whole sample. Moreover, besides the implications for developing research policy and scientist collaboration, our study can capture the causal effect between author collaboration patterns and citation frequency to reveal their causal effects.     

Harnessing the Potential of Artificial Intelligence to Foster Citizens’ Satisfaction: An empirical study on India

Governments are increasingly employing artificial intelligence (AI) enabled services though this is still a relatively new concept that is in nascent stages of implementation. Despite growing emphasis by governments on employing AI-enabled services, many citizens are skeptical of their benefits; this makes an analysis of AI-enabled services an important area of research, especially from the perspective of citizens. This paper employs IT assimilation theory and public value theory to develop a theoretical model that examines whether the introduction of AI-enabled services would generate public value for citizens in India. The model employs the Partial Least Square-Structural Equation Modeling (PLS-SEM) technique to examine how risk factors impact the uptake of AI-enabled services in India. Based on 315 interviews conducted in India, the study highlights that the breadth and depth assimilation of AI-enabled services positively impacts and enhances the satisfaction of citizens, which in turn generates public value.     

All the Polling (Data) That’s Fit to Print? An Analysis of Online News Coverage of 2016 Primary Polls

As U.S. news outlets grapple with the challenges of delivering news in a digital era, journalists cover elections with tighter deadlines and fewer resources. Consequently, we are seeing an explosion in coverage of polls, which require little original reporting and attract readers through their “horse race” appeal. As the number of polls increases, news professionals are culling data from a wider spectrum of sources that vary in methodology and credibility. What remains unclear is how effective the news media are in providing polling context in their online coverage that is less limited by the space and time constraints of more traditional mediums. Utilizing the 2016 U.S. primaries, this exploratory study examines online news articles focused on polls to evaluate the quality of digital coverage across national news outlets.

Keywords: Campaigns and Elections; Content Analysis; Journalism; News Media; Political Communication     

Do altmetrics assess societal impact in a comparable way to case studies? An empirical test of the convergent validity of altmetrics based on data from the UK research excellence framework (REF)

Altmetrics have been proposed as a way to assess the societal impact of research. Although altmetrics are already in use as impact or attention metrics in different contexts, it is still not clear whether they really capture or reflect societal impact. This study is based on altmetrics, citation counts, research output and case study data from the UK Research Excellence Framework (REF), and peers’ REF assessments of research output and societal impact. We investigated the convergent validity of altmetrics by using two REF datasets: publications submitted as research output (PRO) to the REF and publications referenced in case studies (PCS). Case studies, which are intended to demonstrate societal impact, should cite the most relevant research papers. We used the MHq’ indicator for assessing impact – an indicator which has been introduced for count data with many zeros. The results of the first part of the analysis show that news media as well as mentions on Facebook, in blogs, in Wikipedia, and in policy-related documents have higher MHq’ values for PCS than for PRO. Thus, the altmetric indicators seem to have convergent validity for these data. In the second part of the analysis, altmetrics have been correlated with REF reviewers’ average scores on PCS. The negative or close to zero correlations question the convergent validity of altmetrics in that context. We suggest that they may capture a different aspect of societal impact (which can be called unknown attention) to that seen by reviewers (who are interested in the causal link between research and action in society).